Overview

Brought to you by YData

Dataset statistics

Number of variables35
Number of observations1009317
Missing cells3941463
Missing cells (%)11.2%
Duplicate rows162778
Duplicate rows (%)16.1%
Total size in memory83.8 MiB
Average record size in memory87.0 B

Variable types

Text1
Categorical28
Numeric6

Alerts

COLE_DEPTO_UBICACION has constant value "ANTIOQUIA" Constant
ESTU_ESTUDIANTE has constant value "ESTUDIANTE" Constant
Dataset has 162778 (16.1%) duplicate rowsDuplicates
COLE_MCPIO_UBICACION has a high cardinality: 177 distinct values High cardinality
ESTU_MCPIO_PRESENTACION has a high cardinality: 161 distinct values High cardinality
ESTU_MCPIO_RESIDE has a high cardinality: 288 distinct values High cardinality
ESTU_NACIONALIDAD has a high cardinality: 56 distinct values High cardinality
ESTU_PAIS_RESIDE has a high cardinality: 56 distinct values High cardinality
DESEMP_INGLES is highly overall correlated with PUNT_INGLESHigh correlation
ESTU_NACIONALIDAD is highly overall correlated with ESTU_PAIS_RESIDEHigh correlation
ESTU_PAIS_RESIDE is highly overall correlated with ESTU_NACIONALIDADHigh correlation
FAMI_TIENECOMPUTADOR is highly overall correlated with FAMI_TIENEINTERNETHigh correlation
FAMI_TIENEINTERNET is highly overall correlated with FAMI_TIENECOMPUTADORHigh correlation
PUNT_C_NATURALES is highly overall correlated with PUNT_GLOBAL and 4 other fieldsHigh correlation
PUNT_GLOBAL is highly overall correlated with PUNT_C_NATURALES and 4 other fieldsHigh correlation
PUNT_INGLES is highly overall correlated with DESEMP_INGLES and 5 other fieldsHigh correlation
PUNT_LECTURA_CRITICA is highly overall correlated with PUNT_C_NATURALES and 4 other fieldsHigh correlation
PUNT_MATEMATICAS is highly overall correlated with PUNT_C_NATURALES and 4 other fieldsHigh correlation
PUNT_SOCIALES_CIUDADANAS is highly overall correlated with PUNT_C_NATURALES and 4 other fieldsHigh correlation
COLE_BILINGUE is highly imbalanced (94.6%) Imbalance
COLE_CALENDARIO is highly imbalanced (87.5%) Imbalance
COLE_GENERO is highly imbalanced (81.0%) Imbalance
COLE_SEDE_PRINCIPAL is highly imbalanced (83.8%) Imbalance
ESTU_ESTADOINVESTIGACION is highly imbalanced (99.7%) Imbalance
ESTU_NACIONALIDAD is highly imbalanced (99.5%) Imbalance
ESTU_PAIS_RESIDE is highly imbalanced (99.5%) Imbalance
ESTU_PRIVADO_LIBERTAD is highly imbalanced (99.5%) Imbalance
COLE_BILINGUE has 119388 (11.8%) missing values Missing
COLE_CARACTER has 12689 (1.3%) missing values Missing
FAMI_CUARTOSHOGAR has 17588 (1.7%) missing values Missing
FAMI_EDUCACIONMADRE has 25042 (2.5%) missing values Missing
FAMI_EDUCACIONPADRE has 25560 (2.5%) missing values Missing
FAMI_ESTRATOVIVIENDA has 28422 (2.8%) missing values Missing
FAMI_PERSONASHOGAR has 17200 (1.7%) missing values Missing
FAMI_TIENEAUTOMOVIL has 18749 (1.9%) missing values Missing
FAMI_TIENECOMPUTADOR has 18237 (1.8%) missing values Missing
FAMI_TIENEINTERNET has 24654 (2.4%) missing values Missing
FAMI_TIENELAVADORA has 17365 (1.7%) missing values Missing
PUNT_INGLES has 976549 (96.8%) missing values Missing
PUNT_MATEMATICAS has 976549 (96.8%) missing values Missing
PUNT_SOCIALES_CIUDADANAS has 414304 (41.0%) missing values Missing
PUNT_C_NATURALES has 414304 (41.0%) missing values Missing
PUNT_LECTURA_CRITICA has 414304 (41.0%) missing values Missing
PUNT_GLOBAL has 414304 (41.0%) missing values Missing

Reproduction

Analysis started2025-05-14 03:19:00.558572
Analysis finished2025-05-14 03:20:27.109233
Duration1 minute and 26.55 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct763966
Distinct (%)75.7%
Missing0
Missing (%)0.0%
Memory size7.7 MiB
2025-05-14T03:20:27.848290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length16
Median length16
Mean length15.997828
Min length14

Characters and Unicode

Total characters16146880
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique601158 ?
Unique (%)59.6%

Sample

1st rowSB11201720518410
2nd rowSB11201940215679
3rd rowSB11201940215679
4th rowSB11201620355612
5th rowSB11201220572895
ValueCountFrequency (%)
sb11201120104844 11
 
< 0.1%
sb11201220123846 11
 
< 0.1%
sb11201020112124 11
 
< 0.1%
sb11201020109565 11
 
< 0.1%
sb11201020105174 11
 
< 0.1%
sb11201120116680 11
 
< 0.1%
sb11201120120473 11
 
< 0.1%
sb11201020113754 11
 
< 0.1%
sb11201220103138 11
 
< 0.1%
sb11201120124533 11
 
< 0.1%
Other values (763956) 1009207
> 99.9%
2025-05-14T03:20:28.719117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3934980
24.4%
0 2832592
17.5%
2 2742528
17.0%
S 1008221
 
6.2%
B 1008221
 
6.2%
4 990503
 
6.1%
5 714371
 
4.4%
3 688871
 
4.3%
9 626432
 
3.9%
6 576539
 
3.6%
Other values (4) 1023622
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16146880
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3934980
24.4%
0 2832592
17.5%
2 2742528
17.0%
S 1008221
 
6.2%
B 1008221
 
6.2%
4 990503
 
6.1%
5 714371
 
4.4%
3 688871
 
4.3%
9 626432
 
3.9%
6 576539
 
3.6%
Other values (4) 1023622
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16146880
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3934980
24.4%
0 2832592
17.5%
2 2742528
17.0%
S 1008221
 
6.2%
B 1008221
 
6.2%
4 990503
 
6.1%
5 714371
 
4.4%
3 688871
 
4.3%
9 626432
 
3.9%
6 576539
 
3.6%
Other values (4) 1023622
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16146880
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3934980
24.4%
0 2832592
17.5%
2 2742528
17.0%
S 1008221
 
6.2%
B 1008221
 
6.2%
4 990503
 
6.1%
5 714371
 
4.4%
3 688871
 
4.3%
9 626432
 
3.9%
6 576539
 
3.6%
Other values (4) 1023622
 
6.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size985.9 KiB
URBANO
877518 
RURAL
131799 

Length

Max length6
Median length6
Mean length5.8694176
Min length5

Characters and Unicode

Total characters5924103
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowURBANO
2nd rowURBANO
3rd rowURBANO
4th rowURBANO
5th rowURBANO

Common Values

ValueCountFrequency (%)
URBANO 877518
86.9%
RURAL 131799
 
13.1%

Length

2025-05-14T03:20:28.827555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-14T03:20:28.888837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
urbano 877518
86.9%
rural 131799
 
13.1%

Most occurring characters

ValueCountFrequency (%)
R 1141116
19.3%
U 1009317
17.0%
A 1009317
17.0%
B 877518
14.8%
N 877518
14.8%
O 877518
14.8%
L 131799
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5924103
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 1141116
19.3%
U 1009317
17.0%
A 1009317
17.0%
B 877518
14.8%
N 877518
14.8%
O 877518
14.8%
L 131799
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5924103
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 1141116
19.3%
U 1009317
17.0%
A 1009317
17.0%
B 877518
14.8%
N 877518
14.8%
O 877518
14.8%
L 131799
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5924103
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 1141116
19.3%
U 1009317
17.0%
A 1009317
17.0%
B 877518
14.8%
N 877518
14.8%
O 877518
14.8%
L 131799
 
2.2%

COLE_BILINGUE
Categorical

Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing119388
Missing (%)11.8%
Memory size985.9 KiB
N
884425 
S
 
5504

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters889929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 884425
87.6%
S 5504
 
0.5%
(Missing) 119388
 
11.8%

Length

2025-05-14T03:20:28.962429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-14T03:20:29.021633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
n 884425
99.4%
s 5504
 
0.6%

Most occurring characters

ValueCountFrequency (%)
N 884425
99.4%
S 5504
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 889929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 884425
99.4%
S 5504
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 889929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 884425
99.4%
S 5504
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 889929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 884425
99.4%
S 5504
 
0.6%

COLE_CALENDARIO
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing229
Missing (%)< 0.1%
Memory size985.9 KiB
A
982844 
OTRO
 
17872
B
 
8372

Length

Max length4
Median length1
Mean length1.0531331
Min length1

Characters and Unicode

Total characters1062704
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 982844
97.4%
OTRO 17872
 
1.8%
B 8372
 
0.8%
(Missing) 229
 
< 0.1%

Length

2025-05-14T03:20:29.099127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-14T03:20:29.168501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a 982844
97.4%
otro 17872
 
1.8%
b 8372
 
0.8%

Most occurring characters

ValueCountFrequency (%)
A 982844
92.5%
O 35744
 
3.4%
T 17872
 
1.7%
R 17872
 
1.7%
B 8372
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1062704
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 982844
92.5%
O 35744
 
3.4%
T 17872
 
1.7%
R 17872
 
1.7%
B 8372
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1062704
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 982844
92.5%
O 35744
 
3.4%
T 17872
 
1.7%
R 17872
 
1.7%
B 8372
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1062704
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 982844
92.5%
O 35744
 
3.4%
T 17872
 
1.7%
R 17872
 
1.7%
B 8372
 
0.8%

COLE_CARACTER
Categorical

Missing 

Distinct4
Distinct (%)< 0.1%
Missing12689
Missing (%)1.3%
Memory size986.0 KiB
ACADÉMICO
544852 
TÉCNICO/ACADÉMICO
402612 
TÉCNICO
 
37796
NO APLICA
 
11368

Length

Max length17
Median length9
Mean length12.155946
Min length7

Characters and Unicode

Total characters12114956
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTÉCNICO/ACADÉMICO
2nd rowTÉCNICO/ACADÉMICO
3rd rowTÉCNICO/ACADÉMICO
4th rowTÉCNICO
5th rowACADÉMICO

Common Values

ValueCountFrequency (%)
ACADÉMICO 544852
54.0%
TÉCNICO/ACADÉMICO 402612
39.9%
TÉCNICO 37796
 
3.7%
NO APLICA 11368
 
1.1%
(Missing) 12689
 
1.3%

Length

2025-05-14T03:20:29.249234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-14T03:20:29.330998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
académico 544852
54.1%
técnico/académico 402612
39.9%
técnico 37796
 
3.7%
no 11368
 
1.1%
aplica 11368
 
1.1%

Most occurring characters

ValueCountFrequency (%)
C 2787112
23.0%
A 1917664
15.8%
I 1399240
11.5%
O 1399240
11.5%
É 1387872
11.5%
M 947464
 
7.8%
D 947464
 
7.8%
N 451776
 
3.7%
T 440408
 
3.6%
/ 402612
 
3.3%
Other values (3) 34104
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12114956
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 2787112
23.0%
A 1917664
15.8%
I 1399240
11.5%
O 1399240
11.5%
É 1387872
11.5%
M 947464
 
7.8%
D 947464
 
7.8%
N 451776
 
3.7%
T 440408
 
3.6%
/ 402612
 
3.3%
Other values (3) 34104
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12114956
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 2787112
23.0%
A 1917664
15.8%
I 1399240
11.5%
O 1399240
11.5%
É 1387872
11.5%
M 947464
 
7.8%
D 947464
 
7.8%
N 451776
 
3.7%
T 440408
 
3.6%
/ 402612
 
3.3%
Other values (3) 34104
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12114956
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 2787112
23.0%
A 1917664
15.8%
I 1399240
11.5%
O 1399240
11.5%
É 1387872
11.5%
M 947464
 
7.8%
D 947464
 
7.8%
N 451776
 
3.7%
T 440408
 
3.6%
/ 402612
 
3.3%
Other values (3) 34104
 
0.3%

COLE_DEPTO_UBICACION
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size985.9 KiB
ANTIOQUIA
1009317 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters9083853
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowANTIOQUIA
2nd rowANTIOQUIA
3rd rowANTIOQUIA
4th rowANTIOQUIA
5th rowANTIOQUIA

Common Values

ValueCountFrequency (%)
ANTIOQUIA 1009317
100.0%

Length

2025-05-14T03:20:29.426391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-14T03:20:29.751824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
antioquia 1009317
100.0%

Most occurring characters

ValueCountFrequency (%)
A 2018634
22.2%
I 2018634
22.2%
N 1009317
11.1%
T 1009317
11.1%
O 1009317
11.1%
Q 1009317
11.1%
U 1009317
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9083853
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 2018634
22.2%
I 2018634
22.2%
N 1009317
11.1%
T 1009317
11.1%
O 1009317
11.1%
Q 1009317
11.1%
U 1009317
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9083853
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 2018634
22.2%
I 2018634
22.2%
N 1009317
11.1%
T 1009317
11.1%
O 1009317
11.1%
Q 1009317
11.1%
U 1009317
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9083853
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 2018634
22.2%
I 2018634
22.2%
N 1009317
11.1%
T 1009317
11.1%
O 1009317
11.1%
Q 1009317
11.1%
U 1009317
11.1%

COLE_GENERO
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size985.9 KiB
MIXTO
961103 
FEMENINO
 
42928
MASCULINO
 
5286

Length

Max length9
Median length5
Mean length5.148544
Min length5

Characters and Unicode

Total characters5196513
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMIXTO
2nd rowMIXTO
3rd rowMIXTO
4th rowMIXTO
5th rowMIXTO

Common Values

ValueCountFrequency (%)
MIXTO 961103
95.2%
FEMENINO 42928
 
4.3%
MASCULINO 5286
 
0.5%

Length

2025-05-14T03:20:29.828006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-14T03:20:29.904173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mixto 961103
95.2%
femenino 42928
 
4.3%
masculino 5286
 
0.5%

Most occurring characters

ValueCountFrequency (%)
M 1009317
19.4%
I 1009317
19.4%
O 1009317
19.4%
X 961103
18.5%
T 961103
18.5%
N 91142
 
1.8%
E 85856
 
1.7%
F 42928
 
0.8%
A 5286
 
0.1%
S 5286
 
0.1%
Other values (3) 15858
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5196513
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 1009317
19.4%
I 1009317
19.4%
O 1009317
19.4%
X 961103
18.5%
T 961103
18.5%
N 91142
 
1.8%
E 85856
 
1.7%
F 42928
 
0.8%
A 5286
 
0.1%
S 5286
 
0.1%
Other values (3) 15858
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5196513
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 1009317
19.4%
I 1009317
19.4%
O 1009317
19.4%
X 961103
18.5%
T 961103
18.5%
N 91142
 
1.8%
E 85856
 
1.7%
F 42928
 
0.8%
A 5286
 
0.1%
S 5286
 
0.1%
Other values (3) 15858
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5196513
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 1009317
19.4%
I 1009317
19.4%
O 1009317
19.4%
X 961103
18.5%
T 961103
18.5%
N 91142
 
1.8%
E 85856
 
1.7%
F 42928
 
0.8%
A 5286
 
0.1%
S 5286
 
0.1%
Other values (3) 15858
 
0.3%

COLE_JORNADA
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size986.0 KiB
MAÑANA
373181 
COMPLETA
217029 
TARDE
171497 
SABATINA
118079 
NOCHE
75556 

Length

Max length8
Median length6
Mean length6.36578
Min length5

Characters and Unicode

Total characters6425090
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSABATINA
2nd rowMAÑANA
3rd rowMAÑANA
4th rowCOMPLETA
5th rowMAÑANA

Common Values

ValueCountFrequency (%)
MAÑANA 373181
37.0%
COMPLETA 217029
21.5%
TARDE 171497
17.0%
SABATINA 118079
 
11.7%
NOCHE 75556
 
7.5%
UNICA 53975
 
5.3%

Length

2025-05-14T03:20:29.993057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-14T03:20:30.089757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mañana 373181
37.0%
completa 217029
21.5%
tarde 171497
17.0%
sabatina 118079
 
11.7%
noche 75556
 
7.5%
unica 53975
 
5.3%

Most occurring characters

ValueCountFrequency (%)
A 1916281
29.8%
N 620791
 
9.7%
M 590210
 
9.2%
T 506605
 
7.9%
E 464082
 
7.2%
Ñ 373181
 
5.8%
C 346560
 
5.4%
O 292585
 
4.6%
P 217029
 
3.4%
L 217029
 
3.4%
Other values (7) 880737
13.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6425090
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1916281
29.8%
N 620791
 
9.7%
M 590210
 
9.2%
T 506605
 
7.9%
E 464082
 
7.2%
Ñ 373181
 
5.8%
C 346560
 
5.4%
O 292585
 
4.6%
P 217029
 
3.4%
L 217029
 
3.4%
Other values (7) 880737
13.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6425090
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1916281
29.8%
N 620791
 
9.7%
M 590210
 
9.2%
T 506605
 
7.9%
E 464082
 
7.2%
Ñ 373181
 
5.8%
C 346560
 
5.4%
O 292585
 
4.6%
P 217029
 
3.4%
L 217029
 
3.4%
Other values (7) 880737
13.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6425090
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1916281
29.8%
N 620791
 
9.7%
M 590210
 
9.2%
T 506605
 
7.9%
E 464082
 
7.2%
Ñ 373181
 
5.8%
C 346560
 
5.4%
O 292585
 
4.6%
P 217029
 
3.4%
L 217029
 
3.4%
Other values (7) 880737
13.7%

COLE_MCPIO_UBICACION
Categorical

High cardinality 

Distinct177
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
MEDELLÍN
329837 
MEDELLIN
120905 
BELLO
69429 
ENVIGADO
 
35957
ITAGÜÍ
 
25683
Other values (172)
427506 

Length

Max length26
Median length8
Mean length8.0353437
Min length4

Characters and Unicode

Total characters8110209
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMEDELLIN
2nd rowITAGÜÍ
3rd rowITAGÜÍ
4th rowMEDELLIN
5th rowMEDELLÍN

Common Values

ValueCountFrequency (%)
MEDELLÍN 329837
32.7%
MEDELLIN 120905
 
12.0%
BELLO 69429
 
6.9%
ENVIGADO 35957
 
3.6%
ITAGÜÍ 25683
 
2.5%
RIONEGRO 23881
 
2.4%
TURBO 21155
 
2.1%
APARTADÓ 14542
 
1.4%
CAUCASIA 13784
 
1.4%
ITAGUI 12419
 
1.2%
Other values (167) 341725
33.9%

Length

2025-05-14T03:20:30.212499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
medellín 329837
28.1%
medellin 120905
 
10.3%
bello 69429
 
5.9%
envigado 35957
 
3.1%
san 29467
 
2.5%
de 28473
 
2.4%
itagüí 25683
 
2.2%
rionegro 23881
 
2.0%
la 23817
 
2.0%
turbo 21155
 
1.8%
Other values (183) 463504
39.5%

Most occurring characters

ValueCountFrequency (%)
E 1284503
15.8%
L 1210895
14.9%
N 705073
8.7%
A 703923
8.7%
D 614443
7.6%
M 514534
 
6.3%
O 429484
 
5.3%
I 398850
 
4.9%
Í 380456
 
4.7%
R 353166
 
4.4%
Other values (22) 1514882
18.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8110209
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1284503
15.8%
L 1210895
14.9%
N 705073
8.7%
A 703923
8.7%
D 614443
7.6%
M 514534
 
6.3%
O 429484
 
5.3%
I 398850
 
4.9%
Í 380456
 
4.7%
R 353166
 
4.4%
Other values (22) 1514882
18.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8110209
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1284503
15.8%
L 1210895
14.9%
N 705073
8.7%
A 703923
8.7%
D 614443
7.6%
M 514534
 
6.3%
O 429484
 
5.3%
I 398850
 
4.9%
Í 380456
 
4.7%
R 353166
 
4.4%
Other values (22) 1514882
18.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8110209
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1284503
15.8%
L 1210895
14.9%
N 705073
8.7%
A 703923
8.7%
D 614443
7.6%
M 514534
 
6.3%
O 429484
 
5.3%
I 398850
 
4.9%
Í 380456
 
4.7%
R 353166
 
4.4%
Other values (22) 1514882
18.7%

COLE_NATURALEZA
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size985.9 KiB
OFICIAL
719356 
NO OFICIAL
289961 

Length

Max length10
Median length7
Mean length7.8618531
Min length7

Characters and Unicode

Total characters7935102
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO OFICIAL
2nd rowOFICIAL
3rd rowOFICIAL
4th rowNO OFICIAL
5th rowOFICIAL

Common Values

ValueCountFrequency (%)
OFICIAL 719356
71.3%
NO OFICIAL 289961
28.7%

Length

2025-05-14T03:20:30.324336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-14T03:20:30.398659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
oficial 1009317
77.7%
no 289961
 
22.3%

Most occurring characters

ValueCountFrequency (%)
I 2018634
25.4%
O 1299278
16.4%
F 1009317
12.7%
C 1009317
12.7%
A 1009317
12.7%
L 1009317
12.7%
N 289961
 
3.7%
289961
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7935102
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 2018634
25.4%
O 1299278
16.4%
F 1009317
12.7%
C 1009317
12.7%
A 1009317
12.7%
L 1009317
12.7%
N 289961
 
3.7%
289961
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7935102
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 2018634
25.4%
O 1299278
16.4%
F 1009317
12.7%
C 1009317
12.7%
A 1009317
12.7%
L 1009317
12.7%
N 289961
 
3.7%
289961
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7935102
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 2018634
25.4%
O 1299278
16.4%
F 1009317
12.7%
C 1009317
12.7%
A 1009317
12.7%
L 1009317
12.7%
N 289961
 
3.7%
289961
 
3.7%

COLE_SEDE_PRINCIPAL
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size985.9 KiB
S
985263 
N
 
24054

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1009317
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 985263
97.6%
N 24054
 
2.4%

Length

2025-05-14T03:20:30.477538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-14T03:20:30.563885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
s 985263
97.6%
n 24054
 
2.4%

Most occurring characters

ValueCountFrequency (%)
S 985263
97.6%
N 24054
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1009317
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 985263
97.6%
N 24054
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1009317
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 985263
97.6%
N 24054
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1009317
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 985263
97.6%
N 24054
 
2.4%

ESTU_ESTADOINVESTIGACION
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size986.0 KiB
PUBLICAR
1008811 
VALIDEZ OFICINA JURÍDICA
 
317
PRESENTE CON LECTURA TARDIA
 
111
NO SE COMPROBO IDENTIDAD DEL EXAMINADO
 
78

Length

Max length38
Median length8
Mean length8.0094331
Min length8

Characters and Unicode

Total characters8084057
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPUBLICAR
2nd rowPUBLICAR
3rd rowPUBLICAR
4th rowPUBLICAR
5th rowPUBLICAR

Common Values

ValueCountFrequency (%)
PUBLICAR 1008811
99.9%
VALIDEZ OFICINA JURÍDICA 317
 
< 0.1%
PRESENTE CON LECTURA TARDIA 111
 
< 0.1%
NO SE COMPROBO IDENTIDAD DEL EXAMINADO 78
 
< 0.1%

Length

2025-05-14T03:20:30.643562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-14T03:20:30.720792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
publicar 1008811
99.8%
validez 317
 
< 0.1%
oficina 317
 
< 0.1%
jurídica 317
 
< 0.1%
presente 111
 
< 0.1%
con 111
 
< 0.1%
lectura 111
 
< 0.1%
tardia 111
 
< 0.1%
no 78
 
< 0.1%
se 78
 
< 0.1%
Other values (4) 312
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
I 1010424
12.5%
A 1010329
12.5%
C 1009745
12.5%
R 1009539
12.5%
L 1009317
12.5%
U 1009239
12.5%
P 1009000
12.5%
B 1008889
12.5%
1357
 
< 0.1%
D 1135
 
< 0.1%
Other values (12) 5083
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8084057
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 1010424
12.5%
A 1010329
12.5%
C 1009745
12.5%
R 1009539
12.5%
L 1009317
12.5%
U 1009239
12.5%
P 1009000
12.5%
B 1008889
12.5%
1357
 
< 0.1%
D 1135
 
< 0.1%
Other values (12) 5083
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8084057
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 1010424
12.5%
A 1010329
12.5%
C 1009745
12.5%
R 1009539
12.5%
L 1009317
12.5%
U 1009239
12.5%
P 1009000
12.5%
B 1008889
12.5%
1357
 
< 0.1%
D 1135
 
< 0.1%
Other values (12) 5083
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8084057
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 1010424
12.5%
A 1010329
12.5%
C 1009745
12.5%
R 1009539
12.5%
L 1009317
12.5%
U 1009239
12.5%
P 1009000
12.5%
B 1008889
12.5%
1357
 
< 0.1%
D 1135
 
< 0.1%
Other values (12) 5083
 
0.1%

ESTU_ESTUDIANTE
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size985.9 KiB
ESTUDIANTE
1009317 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters10093170
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESTUDIANTE
2nd rowESTUDIANTE
3rd rowESTUDIANTE
4th rowESTUDIANTE
5th rowESTUDIANTE

Common Values

ValueCountFrequency (%)
ESTUDIANTE 1009317
100.0%

Length

2025-05-14T03:20:30.817935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-14T03:20:30.878561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
estudiante 1009317
100.0%

Most occurring characters

ValueCountFrequency (%)
E 2018634
20.0%
T 2018634
20.0%
S 1009317
10.0%
U 1009317
10.0%
D 1009317
10.0%
I 1009317
10.0%
A 1009317
10.0%
N 1009317
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10093170
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 2018634
20.0%
T 2018634
20.0%
S 1009317
10.0%
U 1009317
10.0%
D 1009317
10.0%
I 1009317
10.0%
A 1009317
10.0%
N 1009317
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10093170
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 2018634
20.0%
T 2018634
20.0%
S 1009317
10.0%
U 1009317
10.0%
D 1009317
10.0%
I 1009317
10.0%
A 1009317
10.0%
N 1009317
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10093170
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 2018634
20.0%
T 2018634
20.0%
S 1009317
10.0%
U 1009317
10.0%
D 1009317
10.0%
I 1009317
10.0%
A 1009317
10.0%
N 1009317
10.0%

ESTU_GENERO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing378
Missing (%)< 0.1%
Memory size985.9 KiB
F
569492 
M
439447 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1008939
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
F 569492
56.4%
M 439447
43.5%
(Missing) 378
 
< 0.1%

Length

2025-05-14T03:20:30.950329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-14T03:20:31.020688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
f 569492
56.4%
m 439447
43.6%

Most occurring characters

ValueCountFrequency (%)
F 569492
56.4%
M 439447
43.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1008939
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 569492
56.4%
M 439447
43.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1008939
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 569492
56.4%
M 439447
43.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1008939
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 569492
56.4%
M 439447
43.6%

ESTU_MCPIO_PRESENTACION
Categorical

High cardinality 

Distinct161
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size1.9 MiB
MEDELLÍN
442907 
BELLO
73169 
ITAGÜÍ
51628 
ENVIGADO
 
39337
RIONEGRO
 
32440
Other values (156)
369833 

Length

Max length25
Median length8
Mean length8.1653569
Min length4

Characters and Unicode

Total characters8241409
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)< 0.1%

Sample

1st rowITAGÜÍ
2nd rowITAGÜÍ
3rd rowITAGÜÍ
4th rowMEDELLÍN
5th rowMEDELLÍN

Common Values

ValueCountFrequency (%)
MEDELLÍN 442907
43.9%
BELLO 73169
 
7.2%
ITAGÜÍ 51628
 
5.1%
ENVIGADO 39337
 
3.9%
RIONEGRO 32440
 
3.2%
APARTADÓ 24478
 
2.4%
TURBO 18351
 
1.8%
COPACABANA 17785
 
1.8%
MARINILLA 15418
 
1.5%
CAUCASIA 14652
 
1.5%
Other values (151) 279149
27.7%

Length

2025-05-14T03:20:31.129675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
medellín 442907
36.8%
bello 73169
 
6.1%
itagüí 51628
 
4.3%
de 44746
 
3.7%
envigado 39337
 
3.3%
rionegro 32440
 
2.7%
santa 27181
 
2.3%
apartadó 24478
 
2.0%
la 19764
 
1.6%
san 18674
 
1.5%
Other values (173) 430581
35.7%

Most occurring characters

ValueCountFrequency (%)
E 1270661
15.4%
L 1191177
14.5%
A 756718
9.2%
N 689908
8.4%
D 619806
 
7.5%
Í 521097
 
6.3%
M 515337
 
6.3%
O 418221
 
5.1%
R 344482
 
4.2%
I 263013
 
3.2%
Other values (22) 1650989
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8241409
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1270661
15.4%
L 1191177
14.5%
A 756718
9.2%
N 689908
8.4%
D 619806
 
7.5%
Í 521097
 
6.3%
M 515337
 
6.3%
O 418221
 
5.1%
R 344482
 
4.2%
I 263013
 
3.2%
Other values (22) 1650989
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8241409
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1270661
15.4%
L 1191177
14.5%
A 756718
9.2%
N 689908
8.4%
D 619806
 
7.5%
Í 521097
 
6.3%
M 515337
 
6.3%
O 418221
 
5.1%
R 344482
 
4.2%
I 263013
 
3.2%
Other values (22) 1650989
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8241409
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1270661
15.4%
L 1191177
14.5%
A 756718
9.2%
N 689908
8.4%
D 619806
 
7.5%
Í 521097
 
6.3%
M 515337
 
6.3%
O 418221
 
5.1%
R 344482
 
4.2%
I 263013
 
3.2%
Other values (22) 1650989
20.0%

ESTU_MCPIO_RESIDE
Categorical

High cardinality 

Distinct288
Distinct (%)< 0.1%
Missing5145
Missing (%)0.5%
Memory size1.9 MiB
MEDELLÍN
443485 
BELLO
72115 
ITAGÜÍ
 
40959
ENVIGADO
 
33582
RIONEGRO
 
21101
Other values (283)
392930 

Length

Max length25
Median length8
Mean length8.0686406
Min length4

Characters and Unicode

Total characters8102303
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)< 0.1%

Sample

1st rowMEDELLÍN
2nd rowITAGÜÍ
3rd rowITAGÜÍ
4th rowMEDELLÍN
5th rowMEDELLÍN

Common Values

ValueCountFrequency (%)
MEDELLÍN 443485
43.9%
BELLO 72115
 
7.1%
ITAGÜÍ 40959
 
4.1%
ENVIGADO 33582
 
3.3%
RIONEGRO 21101
 
2.1%
TURBO 20674
 
2.0%
APARTADÓ 19640
 
1.9%
CAUCASIA 13544
 
1.3%
COPACABANA 11884
 
1.2%
CALDAS 10622
 
1.1%
Other values (278) 316566
31.4%

Length

2025-05-14T03:20:31.251396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
medellín 443485
37.6%
bello 72115
 
6.1%
itagüí 40959
 
3.5%
envigado 33582
 
2.9%
de 31185
 
2.6%
san 29673
 
2.5%
la 21811
 
1.9%
rionegro 21101
 
1.8%
turbo 20674
 
1.8%
el 20215
 
1.7%
Other values (304) 443282
37.6%

Most occurring characters

ValueCountFrequency (%)
E 1274994
15.7%
L 1204808
14.9%
A 696981
8.6%
N 694947
8.6%
D 610329
 
7.5%
Í 520271
 
6.4%
M 507932
 
6.3%
O 411346
 
5.1%
R 351679
 
4.3%
I 257022
 
3.2%
Other values (23) 1571994
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8102303
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1274994
15.7%
L 1204808
14.9%
A 696981
8.6%
N 694947
8.6%
D 610329
 
7.5%
Í 520271
 
6.4%
M 507932
 
6.3%
O 411346
 
5.1%
R 351679
 
4.3%
I 257022
 
3.2%
Other values (23) 1571994
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8102303
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1274994
15.7%
L 1204808
14.9%
A 696981
8.6%
N 694947
8.6%
D 610329
 
7.5%
Í 520271
 
6.4%
M 507932
 
6.3%
O 411346
 
5.1%
R 351679
 
4.3%
I 257022
 
3.2%
Other values (23) 1571994
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8102303
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1274994
15.7%
L 1204808
14.9%
A 696981
8.6%
N 694947
8.6%
D 610329
 
7.5%
Í 520271
 
6.4%
M 507932
 
6.3%
O 411346
 
5.1%
R 351679
 
4.3%
I 257022
 
3.2%
Other values (23) 1571994
19.4%

ESTU_NACIONALIDAD
Categorical

High cardinality  High correlation  Imbalance 

Distinct56
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size988.3 KiB
COLOMBIA
1006377 
VENEZUELA
 
2580
ESTADOS UNIDOS
 
121
ESPAÑA
 
26
ECUADOR
 
24
Other values (51)
 
189

Length

Max length40
Median length8
Mean length8.0031219
Min length4

Characters and Unicode

Total characters8077687
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)< 0.1%

Sample

1st rowCOLOMBIA
2nd rowCOLOMBIA
3rd rowCOLOMBIA
4th rowCOLOMBIA
5th rowCOLOMBIA

Common Values

ValueCountFrequency (%)
COLOMBIA 1006377
99.7%
VENEZUELA 2580
 
0.3%
ESTADOS UNIDOS 121
 
< 0.1%
ESPAÑA 26
 
< 0.1%
ECUADOR 24
 
< 0.1%
PANAMÁ 20
 
< 0.1%
PERÚ 16
 
< 0.1%
ARGENTINA 16
 
< 0.1%
BRASIL 15
 
< 0.1%
ITALIA 12
 
< 0.1%
Other values (46) 110
 
< 0.1%

Length

2025-05-14T03:20:31.381516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
colombia 1006377
99.7%
venezuela 2580
 
0.3%
estados 121
 
< 0.1%
unidos 121
 
< 0.1%
españa 26
 
< 0.1%
ecuador 24
 
< 0.1%
panamá 20
 
< 0.1%
perú 16
 
< 0.1%
argentina 16
 
< 0.1%
brasil 15
 
< 0.1%
Other values (64) 163
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
O 2013089
24.9%
A 1009419
12.5%
L 1009033
12.5%
I 1006634
12.5%
C 1006469
12.5%
M 1006426
12.5%
B 1006417
12.5%
E 8000
 
0.1%
N 2799
 
< 0.1%
U 2781
 
< 0.1%
Other values (21) 6620
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8077687
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 2013089
24.9%
A 1009419
12.5%
L 1009033
12.5%
I 1006634
12.5%
C 1006469
12.5%
M 1006426
12.5%
B 1006417
12.5%
E 8000
 
0.1%
N 2799
 
< 0.1%
U 2781
 
< 0.1%
Other values (21) 6620
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8077687
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 2013089
24.9%
A 1009419
12.5%
L 1009033
12.5%
I 1006634
12.5%
C 1006469
12.5%
M 1006426
12.5%
B 1006417
12.5%
E 8000
 
0.1%
N 2799
 
< 0.1%
U 2781
 
< 0.1%
Other values (21) 6620
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8077687
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 2013089
24.9%
A 1009419
12.5%
L 1009033
12.5%
I 1006634
12.5%
C 1006469
12.5%
M 1006426
12.5%
B 1006417
12.5%
E 8000
 
0.1%
N 2799
 
< 0.1%
U 2781
 
< 0.1%
Other values (21) 6620
 
0.1%

ESTU_PAIS_RESIDE
Categorical

High cardinality  High correlation  Imbalance 

Distinct56
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size988.3 KiB
COLOMBIA
1006377 
VENEZUELA
 
2580
ESTADOS UNIDOS
 
121
ESPAÑA
 
26
ECUADOR
 
24
Other values (51)
 
189

Length

Max length40
Median length8
Mean length8.0031219
Min length4

Characters and Unicode

Total characters8077687
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)< 0.1%

Sample

1st rowCOLOMBIA
2nd rowCOLOMBIA
3rd rowCOLOMBIA
4th rowCOLOMBIA
5th rowCOLOMBIA

Common Values

ValueCountFrequency (%)
COLOMBIA 1006377
99.7%
VENEZUELA 2580
 
0.3%
ESTADOS UNIDOS 121
 
< 0.1%
ESPAÑA 26
 
< 0.1%
ECUADOR 24
 
< 0.1%
PANAMÁ 20
 
< 0.1%
PERÚ 16
 
< 0.1%
ARGENTINA 16
 
< 0.1%
BRASIL 15
 
< 0.1%
ITALIA 12
 
< 0.1%
Other values (46) 110
 
< 0.1%

Length

2025-05-14T03:20:31.509062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
colombia 1006377
99.7%
venezuela 2580
 
0.3%
estados 121
 
< 0.1%
unidos 121
 
< 0.1%
españa 26
 
< 0.1%
ecuador 24
 
< 0.1%
panamá 20
 
< 0.1%
perú 16
 
< 0.1%
argentina 16
 
< 0.1%
brasil 15
 
< 0.1%
Other values (64) 163
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
O 2013089
24.9%
A 1009419
12.5%
L 1009033
12.5%
I 1006634
12.5%
C 1006469
12.5%
M 1006426
12.5%
B 1006417
12.5%
E 8000
 
0.1%
N 2799
 
< 0.1%
U 2781
 
< 0.1%
Other values (21) 6620
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8077687
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 2013089
24.9%
A 1009419
12.5%
L 1009033
12.5%
I 1006634
12.5%
C 1006469
12.5%
M 1006426
12.5%
B 1006417
12.5%
E 8000
 
0.1%
N 2799
 
< 0.1%
U 2781
 
< 0.1%
Other values (21) 6620
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8077687
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 2013089
24.9%
A 1009419
12.5%
L 1009033
12.5%
I 1006634
12.5%
C 1006469
12.5%
M 1006426
12.5%
B 1006417
12.5%
E 8000
 
0.1%
N 2799
 
< 0.1%
U 2781
 
< 0.1%
Other values (21) 6620
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8077687
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 2013089
24.9%
A 1009419
12.5%
L 1009033
12.5%
I 1006634
12.5%
C 1006469
12.5%
M 1006426
12.5%
B 1006417
12.5%
E 8000
 
0.1%
N 2799
 
< 0.1%
U 2781
 
< 0.1%
Other values (21) 6620
 
0.1%

ESTU_PRIVADO_LIBERTAD
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size985.9 KiB
N
1008948 
S
 
369

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1009317
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 1008948
> 99.9%
S 369
 
< 0.1%

Length

2025-05-14T03:20:31.634127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-14T03:20:31.692834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
n 1008948
> 99.9%
s 369
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 1008948
> 99.9%
S 369
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1009317
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1008948
> 99.9%
S 369
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1009317
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1008948
> 99.9%
S 369
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1009317
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1008948
> 99.9%
S 369
 
< 0.1%

FAMI_CUARTOSHOGAR
Categorical

Missing 

Distinct11
Distinct (%)< 0.1%
Missing17588
Missing (%)1.7%
Memory size986.2 KiB
Tres
416656 
Dos
345502 
Cuatro
132854 
Uno
47420 
Cinco
 
33948
Other values (6)
 
15349

Length

Max length10
Median length6
Mean length3.9418581
Min length3

Characters and Unicode

Total characters3909255
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCinco
2nd rowUno
3rd rowUno
4th rowTres
5th rowTres

Common Values

ValueCountFrequency (%)
Tres 416656
41.3%
Dos 345502
34.2%
Cuatro 132854
 
13.2%
Uno 47420
 
4.7%
Cinco 33948
 
3.4%
Seis 6373
 
0.6%
Seis o mas 5007
 
0.5%
Siete 2170
 
0.2%
Ocho 921
 
0.1%
Diez o más 503
 
< 0.1%
(Missing) 17588
 
1.7%

Length

2025-05-14T03:20:31.772822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tres 416656
41.6%
dos 345502
34.5%
cuatro 132854
 
13.2%
uno 47420
 
4.7%
cinco 33948
 
3.4%
seis 11380
 
1.1%
o 5510
 
0.5%
mas 5007
 
0.5%
siete 2170
 
0.2%
ocho 921
 
0.1%
Other values (3) 1381
 
0.1%

Most occurring characters

ValueCountFrequency (%)
s 779048
19.9%
o 566155
14.5%
r 549510
14.1%
e 433629
11.1%
T 416656
10.7%
D 346005
8.9%
C 166802
 
4.3%
a 137861
 
3.5%
t 135024
 
3.5%
u 133229
 
3.4%
Other values (13) 245336
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3909255
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 779048
19.9%
o 566155
14.5%
r 549510
14.1%
e 433629
11.1%
T 416656
10.7%
D 346005
8.9%
C 166802
 
4.3%
a 137861
 
3.5%
t 135024
 
3.5%
u 133229
 
3.4%
Other values (13) 245336
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3909255
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 779048
19.9%
o 566155
14.5%
r 549510
14.1%
e 433629
11.1%
T 416656
10.7%
D 346005
8.9%
C 166802
 
4.3%
a 137861
 
3.5%
t 135024
 
3.5%
u 133229
 
3.4%
Other values (13) 245336
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3909255
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 779048
19.9%
o 566155
14.5%
r 549510
14.1%
e 433629
11.1%
T 416656
10.7%
D 346005
8.9%
C 166802
 
4.3%
a 137861
 
3.5%
t 135024
 
3.5%
u 133229
 
3.4%
Other values (13) 245336
 
6.3%

FAMI_EDUCACIONMADRE
Categorical

Missing 

Distinct12
Distinct (%)< 0.1%
Missing25042
Missing (%)2.5%
Memory size986.2 KiB
Secundaria (Bachillerato) completa
263740 
Primaria incompleta
160669 
Secundaria (Bachillerato) incompleta
146928 
Primaria completa
135706 
Técnica o tecnológica completa
86404 
Other values (7)
190828 

Length

Max length36
Median length32
Mean length26.800659
Min length7

Characters and Unicode

Total characters26379219
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSecundaria (Bachillerato) incompleta
2nd rowSecundaria (Bachillerato) completa
3rd rowSecundaria (Bachillerato) completa
4th rowTécnica o tecnológica completa
5th rowPrimaria incompleta

Common Values

ValueCountFrequency (%)
Secundaria (Bachillerato) completa 263740
26.1%
Primaria incompleta 160669
15.9%
Secundaria (Bachillerato) incompleta 146928
14.6%
Primaria completa 135706
13.4%
Técnica o tecnológica completa 86404
 
8.6%
Educación profesional completa 82537
 
8.2%
Ninguno 26712
 
2.6%
No sabe 25093
 
2.5%
Técnica o tecnológica incompleta 21508
 
2.1%
Postgrado 19851
 
2.0%
Other values (2) 15127
 
1.5%
(Missing) 25042
 
2.5%

Length

2025-05-14T03:20:31.871907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
completa 568387
21.5%
secundaria 410668
15.5%
bachillerato 410668
15.5%
incompleta 343410
13.0%
primaria 296375
11.2%
técnica 107912
 
4.1%
o 107912
 
4.1%
tecnológica 107912
 
4.1%
educación 96842
 
3.7%
profesional 96842
 
3.7%
Other values (5) 98393
 
3.7%

Most occurring characters

ValueCountFrequency (%)
a 3602493
13.7%
c 2359287
 
8.9%
i 2194538
 
8.3%
e 1962980
 
7.4%
l 1938709
 
7.3%
o 1824302
 
6.9%
1661046
 
6.3%
r 1530779
 
5.8%
t 1450228
 
5.5%
n 1217010
 
4.6%
Other values (20) 6637847
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26379219
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3602493
13.7%
c 2359287
 
8.9%
i 2194538
 
8.3%
e 1962980
 
7.4%
l 1938709
 
7.3%
o 1824302
 
6.9%
1661046
 
6.3%
r 1530779
 
5.8%
t 1450228
 
5.5%
n 1217010
 
4.6%
Other values (20) 6637847
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26379219
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3602493
13.7%
c 2359287
 
8.9%
i 2194538
 
8.3%
e 1962980
 
7.4%
l 1938709
 
7.3%
o 1824302
 
6.9%
1661046
 
6.3%
r 1530779
 
5.8%
t 1450228
 
5.5%
n 1217010
 
4.6%
Other values (20) 6637847
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26379219
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3602493
13.7%
c 2359287
 
8.9%
i 2194538
 
8.3%
e 1962980
 
7.4%
l 1938709
 
7.3%
o 1824302
 
6.9%
1661046
 
6.3%
r 1530779
 
5.8%
t 1450228
 
5.5%
n 1217010
 
4.6%
Other values (20) 6637847
25.2%

FAMI_EDUCACIONPADRE
Categorical

Missing 

Distinct12
Distinct (%)< 0.1%
Missing25560
Missing (%)2.5%
Memory size986.2 KiB
Secundaria (Bachillerato) completa
213724 
Primaria incompleta
199172 
Primaria completa
132007 
Secundaria (Bachillerato) incompleta
131380 
Educación profesional completa
74103 
Other values (7)
233371 

Length

Max length36
Median length32
Mean length24.247447
Min length7

Characters and Unicode

Total characters23853596
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrimaria incompleta
2nd rowPrimaria incompleta
3rd rowPrimaria incompleta
4th rowSecundaria (Bachillerato) completa
5th rowPrimaria incompleta

Common Values

ValueCountFrequency (%)
Secundaria (Bachillerato) completa 213724
21.2%
Primaria incompleta 199172
19.7%
Primaria completa 132007
13.1%
Secundaria (Bachillerato) incompleta 131380
13.0%
Educación profesional completa 74103
 
7.3%
No sabe 72546
 
7.2%
Técnica o tecnológica completa 55510
 
5.5%
Ninguno 52381
 
5.2%
Postgrado 20921
 
2.1%
Técnica o tecnológica incompleta 13797
 
1.4%
Other values (2) 18216
 
1.8%
(Missing) 25560
 
2.5%

Length

2025-05-14T03:20:31.985712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
completa 475344
19.3%
incompleta 356188
14.5%
secundaria 345104
14.0%
bachillerato 345104
14.0%
primaria 331179
13.4%
educación 85942
 
3.5%
profesional 85942
 
3.5%
no 78923
 
3.2%
sabe 72546
 
2.9%
técnica 69307
 
2.8%
Other values (5) 218293
8.9%

Most occurring characters

ValueCountFrequency (%)
a 3284648
13.8%
i 2078010
 
8.7%
c 1977229
 
8.3%
e 1749535
 
7.3%
l 1683366
 
7.1%
o 1660280
 
7.0%
1480115
 
6.2%
r 1459429
 
6.1%
t 1266864
 
5.3%
m 1162711
 
4.9%
Other values (20) 6051409
25.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23853596
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3284648
13.8%
i 2078010
 
8.7%
c 1977229
 
8.3%
e 1749535
 
7.3%
l 1683366
 
7.1%
o 1660280
 
7.0%
1480115
 
6.2%
r 1459429
 
6.1%
t 1266864
 
5.3%
m 1162711
 
4.9%
Other values (20) 6051409
25.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23853596
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3284648
13.8%
i 2078010
 
8.7%
c 1977229
 
8.3%
e 1749535
 
7.3%
l 1683366
 
7.1%
o 1660280
 
7.0%
1480115
 
6.2%
r 1459429
 
6.1%
t 1266864
 
5.3%
m 1162711
 
4.9%
Other values (20) 6051409
25.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23853596
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3284648
13.8%
i 2078010
 
8.7%
c 1977229
 
8.3%
e 1749535
 
7.3%
l 1683366
 
7.1%
o 1660280
 
7.0%
1480115
 
6.2%
r 1459429
 
6.1%
t 1266864
 
5.3%
m 1162711
 
4.9%
Other values (20) 6051409
25.4%

FAMI_ESTRATOVIVIENDA
Categorical

Missing 

Distinct7
Distinct (%)< 0.1%
Missing28422
Missing (%)2.8%
Memory size986.1 KiB
Estrato 2
393933 
Estrato 3
252831 
Estrato 1
231783 
Estrato 4
51829 
Estrato 5
 
28192
Other values (2)
 
22327

Length

Max length11
Median length9
Mean length9.0136916
Min length9

Characters and Unicode

Total characters8841485
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEstrato 2
2nd rowEstrato 3
3rd rowEstrato 3
4th rowEstrato 3
5th rowEstrato 3

Common Values

ValueCountFrequency (%)
Estrato 2 393933
39.0%
Estrato 3 252831
25.0%
Estrato 1 231783
23.0%
Estrato 4 51829
 
5.1%
Estrato 5 28192
 
2.8%
Estrato 6 15612
 
1.5%
Sin Estrato 6715
 
0.7%
(Missing) 28422
 
2.8%

Length

2025-05-14T03:20:32.097701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-14T03:20:32.190990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
estrato 980895
50.0%
2 393933
20.1%
3 252831
 
12.9%
1 231783
 
11.8%
4 51829
 
2.6%
5 28192
 
1.4%
6 15612
 
0.8%
sin 6715
 
0.3%

Most occurring characters

ValueCountFrequency (%)
t 1961790
22.2%
E 980895
11.1%
s 980895
11.1%
r 980895
11.1%
a 980895
11.1%
o 980895
11.1%
980895
11.1%
2 393933
 
4.5%
3 252831
 
2.9%
1 231783
 
2.6%
Other values (6) 115778
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8841485
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 1961790
22.2%
E 980895
11.1%
s 980895
11.1%
r 980895
11.1%
a 980895
11.1%
o 980895
11.1%
980895
11.1%
2 393933
 
4.5%
3 252831
 
2.9%
1 231783
 
2.6%
Other values (6) 115778
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8841485
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 1961790
22.2%
E 980895
11.1%
s 980895
11.1%
r 980895
11.1%
a 980895
11.1%
o 980895
11.1%
980895
11.1%
2 393933
 
4.5%
3 252831
 
2.9%
1 231783
 
2.6%
Other values (6) 115778
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8841485
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 1961790
22.2%
E 980895
11.1%
s 980895
11.1%
r 980895
11.1%
a 980895
11.1%
o 980895
11.1%
980895
11.1%
2 393933
 
4.5%
3 252831
 
2.9%
1 231783
 
2.6%
Other values (6) 115778
 
1.3%

FAMI_PERSONASHOGAR
Categorical

Missing 

Distinct17
Distinct (%)< 0.1%
Missing17200
Missing (%)1.7%
Memory size986.5 KiB
Cuatro
198671 
3 a 4
194020 
Cinco
134483 
Tres
122726 
5 a 6
98065 
Other values (12)
244152 

Length

Max length10
Median length5
Mean length4.9320766
Min length3

Characters and Unicode

Total characters4893197
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5 a 6
2nd row3 a 4
3rd row3 a 4
4th rowCuatro
5th rowCuatro

Common Values

ValueCountFrequency (%)
Cuatro 198671
19.7%
3 a 4 194020
19.2%
Cinco 134483
13.3%
Tres 122726
12.2%
5 a 6 98065
9.7%
Seis 66919
 
6.6%
Dos 39331
 
3.9%
1 a 2 35868
 
3.6%
Siete 32425
 
3.2%
7 a 8 22669
 
2.2%
Other values (7) 46940
 
4.7%

Length

2025-05-14T03:20:32.312369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 350622
20.4%
cuatro 198671
11.6%
3 194020
11.3%
4 194020
11.3%
cinco 134483
 
7.8%
tres 122726
 
7.2%
5 98065
 
5.7%
6 98065
 
5.7%
seis 66919
 
3.9%
dos 39331
 
2.3%
Other values (14) 219117
12.8%

Most occurring characters

ValueCountFrequency (%)
723922
14.8%
a 552604
11.3%
o 404553
 
8.3%
C 333154
 
6.8%
r 321397
 
6.6%
e 281009
 
5.7%
s 240315
 
4.9%
i 238743
 
4.9%
t 231096
 
4.7%
u 206566
 
4.2%
Other values (22) 1359838
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4893197
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
723922
14.8%
a 552604
11.3%
o 404553
 
8.3%
C 333154
 
6.8%
r 321397
 
6.6%
e 281009
 
5.7%
s 240315
 
4.9%
i 238743
 
4.9%
t 231096
 
4.7%
u 206566
 
4.2%
Other values (22) 1359838
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4893197
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
723922
14.8%
a 552604
11.3%
o 404553
 
8.3%
C 333154
 
6.8%
r 321397
 
6.6%
e 281009
 
5.7%
s 240315
 
4.9%
i 238743
 
4.9%
t 231096
 
4.7%
u 206566
 
4.2%
Other values (22) 1359838
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4893197
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
723922
14.8%
a 552604
11.3%
o 404553
 
8.3%
C 333154
 
6.8%
r 321397
 
6.6%
e 281009
 
5.7%
s 240315
 
4.9%
i 238743
 
4.9%
t 231096
 
4.7%
u 206566
 
4.2%
Other values (22) 1359838
27.8%

FAMI_TIENEAUTOMOVIL
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing18749
Missing (%)1.9%
Memory size985.9 KiB
No
788052 
Si
202516 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1981136
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 788052
78.1%
Si 202516
 
20.1%
(Missing) 18749
 
1.9%

Length

2025-05-14T03:20:32.416581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-14T03:20:32.479791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 788052
79.6%
si 202516
 
20.4%

Most occurring characters

ValueCountFrequency (%)
N 788052
39.8%
o 788052
39.8%
S 202516
 
10.2%
i 202516
 
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1981136
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 788052
39.8%
o 788052
39.8%
S 202516
 
10.2%
i 202516
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1981136
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 788052
39.8%
o 788052
39.8%
S 202516
 
10.2%
i 202516
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1981136
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 788052
39.8%
o 788052
39.8%
S 202516
 
10.2%
i 202516
 
10.2%

FAMI_TIENECOMPUTADOR
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing18237
Missing (%)1.8%
Memory size985.9 KiB
Si
623950 
No
367130 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1982160
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowSi
3rd rowSi
4th rowSi
5th rowSi

Common Values

ValueCountFrequency (%)
Si 623950
61.8%
No 367130
36.4%
(Missing) 18237
 
1.8%

Length

2025-05-14T03:20:32.564928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-14T03:20:32.646845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
si 623950
63.0%
no 367130
37.0%

Most occurring characters

ValueCountFrequency (%)
S 623950
31.5%
i 623950
31.5%
N 367130
18.5%
o 367130
18.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1982160
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 623950
31.5%
i 623950
31.5%
N 367130
18.5%
o 367130
18.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1982160
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 623950
31.5%
i 623950
31.5%
N 367130
18.5%
o 367130
18.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1982160
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 623950
31.5%
i 623950
31.5%
N 367130
18.5%
o 367130
18.5%

FAMI_TIENEINTERNET
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing24654
Missing (%)2.4%
Memory size985.9 KiB
Si
605712 
No
378951 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1969326
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSi
2nd rowSi
3rd rowSi
4th rowSi
5th rowNo

Common Values

ValueCountFrequency (%)
Si 605712
60.0%
No 378951
37.5%
(Missing) 24654
 
2.4%

Length

2025-05-14T03:20:32.722562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-14T03:20:32.785700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
si 605712
61.5%
no 378951
38.5%

Most occurring characters

ValueCountFrequency (%)
S 605712
30.8%
i 605712
30.8%
N 378951
19.2%
o 378951
19.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1969326
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 605712
30.8%
i 605712
30.8%
N 378951
19.2%
o 378951
19.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1969326
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 605712
30.8%
i 605712
30.8%
N 378951
19.2%
o 378951
19.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1969326
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 605712
30.8%
i 605712
30.8%
N 378951
19.2%
o 378951
19.2%

FAMI_TIENELAVADORA
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing17365
Missing (%)1.7%
Memory size985.9 KiB
Si
743285 
No
248667 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1983904
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSi
2nd rowSi
3rd rowSi
4th rowSi
5th rowSi

Common Values

ValueCountFrequency (%)
Si 743285
73.6%
No 248667
 
24.6%
(Missing) 17365
 
1.7%

Length

2025-05-14T03:20:32.865368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-14T03:20:32.935859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
si 743285
74.9%
no 248667
 
25.1%

Most occurring characters

ValueCountFrequency (%)
S 743285
37.5%
i 743285
37.5%
N 248667
 
12.5%
o 248667
 
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1983904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 743285
37.5%
i 743285
37.5%
N 248667
 
12.5%
o 248667
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1983904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 743285
37.5%
i 743285
37.5%
N 248667
 
12.5%
o 248667
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1983904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 743285
37.5%
i 743285
37.5%
N 248667
 
12.5%
o 248667
 
12.5%

DESEMP_INGLES
Categorical

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing500
Missing (%)< 0.1%
Memory size986.0 KiB
A-
537688 
A1
286559 
A2
99365 
B1
61365 
B+
 
23838

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2017634
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA-
2nd rowA1
3rd rowA1
4th rowB1
5th rowA1

Common Values

ValueCountFrequency (%)
A- 537688
53.3%
A1 286559
28.4%
A2 99365
 
9.8%
B1 61365
 
6.1%
B+ 23838
 
2.4%
-1 2
 
< 0.1%
(Missing) 500
 
< 0.1%

Length

2025-05-14T03:20:33.012823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-14T03:20:33.093426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a 537688
53.3%
a1 286559
28.4%
a2 99365
 
9.8%
b1 61365
 
6.1%
b 23838
 
2.4%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 923612
45.8%
- 537690
26.6%
1 347926
 
17.2%
2 99365
 
4.9%
B 85203
 
4.2%
+ 23838
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2017634
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 923612
45.8%
- 537690
26.6%
1 347926
 
17.2%
2 99365
 
4.9%
B 85203
 
4.2%
+ 23838
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2017634
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 923612
45.8%
- 537690
26.6%
1 347926
 
17.2%
2 99365
 
4.9%
B 85203
 
4.2%
+ 23838
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2017634
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 923612
45.8%
- 537690
26.6%
1 347926
 
17.2%
2 99365
 
4.9%
B 85203
 
4.2%
+ 23838
 
1.2%

PUNT_INGLES
Real number (ℝ)

High correlation  Missing 

Distinct222
Distinct (%)0.7%
Missing976549
Missing (%)96.8%
Infinite0
Infinite (%)0.0%
Mean47.646595
Minimum-1
Maximum116.95
Zeros32
Zeros (%)< 0.1%
Negative10
Negative (%)< 0.1%
Memory size7.7 MiB
2025-05-14T03:20:33.210570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile32
Q140
median45
Q353
95-th percentile73.24
Maximum116.95
Range117.95
Interquartile range (IQR)13

Descriptive statistics

Standard deviation12.488978
Coefficient of variation (CV)0.26211691
Kurtosis2.5432019
Mean47.646595
Median Absolute Deviation (MAD)6
Skewness1.1491096
Sum1561283.6
Variance155.97458
MonotonicityNot monotonic
2025-05-14T03:20:33.339572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43 2480
 
0.2%
39 1303
 
0.1%
45 1264
 
0.1%
40 1170
 
0.1%
41 1097
 
0.1%
49 1029
 
0.1%
47 1001
 
0.1%
42 990
 
0.1%
46 886
 
0.1%
36 883
 
0.1%
Other values (212) 20665
 
2.0%
(Missing) 976549
96.8%
ValueCountFrequency (%)
-1 10
 
< 0.1%
0 32
< 0.1%
3 2
 
< 0.1%
6 1
 
< 0.1%
7 2
 
< 0.1%
10 1
 
< 0.1%
14 9
 
< 0.1%
15 1
 
< 0.1%
17 4
 
< 0.1%
17.39 1
 
< 0.1%
ValueCountFrequency (%)
116.95 5
 
< 0.1%
102.96 18
 
< 0.1%
102.61 5
 
< 0.1%
100 91
< 0.1%
97 12
 
< 0.1%
96 15
 
< 0.1%
95.85 2
 
< 0.1%
95 5
 
< 0.1%
94.41 31
 
< 0.1%
94.05 21
 
< 0.1%

PUNT_MATEMATICAS
Real number (ℝ)

High correlation  Missing 

Distinct215
Distinct (%)0.7%
Missing976549
Missing (%)96.8%
Infinite0
Infinite (%)0.0%
Mean48.03038
Minimum0
Maximum107
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2025-05-14T03:20:33.475533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q140
median48
Q355
95-th percentile69
Maximum107
Range107
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.97784
Coefficient of variation (CV)0.24938049
Kurtosis0.62451704
Mean48.03038
Median Absolute Deviation (MAD)8
Skewness0.26334531
Sum1573859.5
Variance143.46864
MonotonicityNot monotonic
2025-05-14T03:20:33.943086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49 1430
 
0.1%
52 1263
 
0.1%
45 1089
 
0.1%
46 1042
 
0.1%
42 1028
 
0.1%
55 973
 
0.1%
43 930
 
0.1%
39 909
 
0.1%
38 900
 
0.1%
40 803
 
0.1%
Other values (205) 22401
 
2.2%
(Missing) 976549
96.8%
ValueCountFrequency (%)
0 9
< 0.1%
1 11
< 0.1%
5 1
 
< 0.1%
6 20
< 0.1%
7 17
< 0.1%
9 1
 
< 0.1%
11 2
 
< 0.1%
12 15
< 0.1%
12.92 2
 
< 0.1%
13 9
< 0.1%
ValueCountFrequency (%)
107 1
 
< 0.1%
100 16
< 0.1%
99.48 2
 
< 0.1%
99 1
 
< 0.1%
98 1
 
< 0.1%
97.66 1
 
< 0.1%
97 7
< 0.1%
96 5
 
< 0.1%
95 3
 
< 0.1%
93.8 1
 
< 0.1%

PUNT_SOCIALES_CIUDADANAS
Real number (ℝ)

High correlation  Missing 

Distinct96
Distinct (%)< 0.1%
Missing414304
Missing (%)41.0%
Infinite0
Infinite (%)0.0%
Mean48.410194
Minimum0
Maximum100
Zeros61
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2025-05-14T03:20:34.084491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q140
median48
Q357
95-th percentile68
Maximum100
Range100
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.66675
Coefficient of variation (CV)0.24099779
Kurtosis-0.44802187
Mean48.410194
Median Absolute Deviation (MAD)9
Skewness0.13427022
Sum28804695
Variance136.11305
MonotonicityNot monotonic
2025-05-14T03:20:34.225594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 20640
 
2.0%
47 19682
 
2.0%
53 19375
 
1.9%
49 19146
 
1.9%
44 19087
 
1.9%
56 18201
 
1.8%
46 17883
 
1.8%
52 17252
 
1.7%
41 16820
 
1.7%
43 16800
 
1.7%
Other values (86) 410127
40.6%
(Missing) 414304
41.0%
ValueCountFrequency (%)
0 61
< 0.1%
1 4
 
< 0.1%
2 4
 
< 0.1%
3 6
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
9 16
 
< 0.1%
10 18
 
< 0.1%
11 4
 
< 0.1%
ValueCountFrequency (%)
100 107
< 0.1%
99 1
 
< 0.1%
98 8
 
< 0.1%
96 1
 
< 0.1%
95 1
 
< 0.1%
94 2
 
< 0.1%
93 13
 
< 0.1%
92 16
 
< 0.1%
91 6
 
< 0.1%
89 3
 
< 0.1%

PUNT_C_NATURALES
Real number (ℝ)

High correlation  Missing 

Distinct87
Distinct (%)< 0.1%
Missing414304
Missing (%)41.0%
Infinite0
Infinite (%)0.0%
Mean48.581718
Minimum0
Maximum100
Zeros73
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2025-05-14T03:20:34.367071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32
Q141
median48
Q356
95-th percentile67
Maximum100
Range100
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.547323
Coefficient of variation (CV)0.21710478
Kurtosis-0.16194506
Mean48.581718
Median Absolute Deviation (MAD)8
Skewness0.27268891
Sum28906754
Variance111.24603
MonotonicityNot monotonic
2025-05-14T03:20:34.508667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 21355
 
2.1%
45 20896
 
2.1%
46 20760
 
2.1%
43 20663
 
2.0%
44 20605
 
2.0%
49 20476
 
2.0%
48 20391
 
2.0%
42 20138
 
2.0%
41 19805
 
2.0%
51 19556
 
1.9%
Other values (77) 390368
38.7%
(Missing) 414304
41.0%
ValueCountFrequency (%)
0 73
< 0.1%
7 1
 
< 0.1%
8 6
 
< 0.1%
13 9
 
< 0.1%
14 13
 
< 0.1%
15 3
 
< 0.1%
16 9
 
< 0.1%
17 41
< 0.1%
18 44
< 0.1%
19 44
< 0.1%
ValueCountFrequency (%)
100 124
< 0.1%
98 1
 
< 0.1%
96 19
 
< 0.1%
95 27
 
< 0.1%
93 14
 
< 0.1%
91 17
 
< 0.1%
90 41
 
< 0.1%
89 7
 
< 0.1%
88 19
 
< 0.1%
87 40
 
< 0.1%

PUNT_LECTURA_CRITICA
Real number (ℝ)

High correlation  Missing 

Distinct86
Distinct (%)< 0.1%
Missing414304
Missing (%)41.0%
Infinite0
Infinite (%)0.0%
Mean52.035687
Minimum0
Maximum100
Zeros194
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2025-05-14T03:20:34.660728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q144
median52
Q360
95-th percentile69
Maximum100
Range100
Interquartile range (IQR)16

Descriptive statistics

Standard deviation10.4284
Coefficient of variation (CV)0.20040861
Kurtosis-0.14392737
Mean52.035687
Median Absolute Deviation (MAD)8
Skewness0.043403747
Sum30961910
Variance108.75152
MonotonicityNot monotonic
2025-05-14T03:20:34.801286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54 23004
 
2.3%
51 22210
 
2.2%
50 22008
 
2.2%
47 21643
 
2.1%
57 20828
 
2.1%
55 20530
 
2.0%
52 20465
 
2.0%
53 19471
 
1.9%
58 19286
 
1.9%
60 19140
 
1.9%
Other values (76) 386428
38.3%
(Missing) 414304
41.0%
ValueCountFrequency (%)
0 194
< 0.1%
7 6
 
< 0.1%
8 8
 
< 0.1%
9 1
 
< 0.1%
10 2
 
< 0.1%
11 2
 
< 0.1%
14 7
 
< 0.1%
15 21
 
< 0.1%
16 25
 
< 0.1%
18 3
 
< 0.1%
ValueCountFrequency (%)
100 181
< 0.1%
97 1
 
< 0.1%
96 1
 
< 0.1%
94 13
 
< 0.1%
93 31
 
< 0.1%
92 34
 
< 0.1%
91 40
 
< 0.1%
88 3
 
< 0.1%
86 2
 
< 0.1%
85 107
< 0.1%

PUNT_GLOBAL
Real number (ℝ)

High correlation  Missing 

Distinct431
Distinct (%)0.1%
Missing414304
Missing (%)41.0%
Infinite0
Infinite (%)0.0%
Mean248.16205
Minimum0
Maximum490
Zeros21
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2025-05-14T03:20:34.938596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile173
Q1210
median245
Q3283
95-th percentile337
Maximum490
Range490
Interquartile range (IQR)73

Descriptive statistics

Standard deviation50.305158
Coefficient of variation (CV)0.20271092
Kurtosis-0.27977684
Mean248.16205
Median Absolute Deviation (MAD)36
Skewness0.33375479
Sum1.4765964 × 108
Variance2530.6089
MonotonicityNot monotonic
2025-05-14T03:20:35.072744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
235 5040
 
0.5%
230 5035
 
0.5%
232 5034
 
0.5%
223 5027
 
0.5%
243 5021
 
0.5%
225 5012
 
0.5%
248 4961
 
0.5%
237 4928
 
0.5%
233 4928
 
0.5%
238 4921
 
0.5%
Other values (421) 545106
54.0%
(Missing) 414304
41.0%
ValueCountFrequency (%)
0 21
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
13 1
 
< 0.1%
14 3
 
< 0.1%
15 1
 
< 0.1%
16 2
 
< 0.1%
17 2
 
< 0.1%
20 1
 
< 0.1%
22 1
 
< 0.1%
ValueCountFrequency (%)
490 1
 
< 0.1%
477 3
< 0.1%
476 2
< 0.1%
475 1
 
< 0.1%
473 2
< 0.1%
472 1
 
< 0.1%
467 1
 
< 0.1%
465 2
< 0.1%
463 1
 
< 0.1%
462 1
 
< 0.1%

Interactions

2025-05-14T03:20:10.770673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:06.064638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:06.753210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:07.428443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:08.304992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:09.225356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:10.920191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:06.180357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:06.859687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:07.535997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:08.430568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:09.331756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:11.146649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:06.296873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:06.978611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:07.696420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:08.601437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:09.884248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:11.412530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:06.423699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:07.089155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:07.863912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:08.766886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:10.113394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:11.660223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:06.533721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:07.196293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:08.028564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:08.934385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:10.349464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:11.901143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:06.643266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:07.303828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:08.197993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:09.118415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-14T03:20:10.608763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-14T03:20:35.214519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
COLE_AREA_UBICACIONCOLE_BILINGUECOLE_CALENDARIOCOLE_CARACTERCOLE_GENEROCOLE_JORNADACOLE_NATURALEZACOLE_SEDE_PRINCIPALDESEMP_INGLESESTU_ESTADOINVESTIGACIONESTU_GENEROESTU_NACIONALIDADESTU_PAIS_RESIDEESTU_PRIVADO_LIBERTADFAMI_CUARTOSHOGARFAMI_EDUCACIONMADREFAMI_EDUCACIONPADREFAMI_ESTRATOVIVIENDAFAMI_PERSONASHOGARFAMI_TIENEAUTOMOVILFAMI_TIENECOMPUTADORFAMI_TIENEINTERNETFAMI_TIENELAVADORAPUNT_C_NATURALESPUNT_GLOBALPUNT_INGLESPUNT_LECTURA_CRITICAPUNT_MATEMATICASPUNT_SOCIALES_CIUDADANAS
COLE_AREA_UBICACION1.0000.0910.1290.1920.0840.2400.0970.1530.0970.0160.0190.0070.0070.0070.0340.1560.1730.1940.0940.0210.1760.1930.1350.0980.1250.0850.1180.0690.111
COLE_BILINGUE0.0911.0000.4450.0570.1210.0780.0720.0110.1810.0030.0130.0300.0300.0000.0280.1130.1270.2110.0220.0840.0290.0320.0230.0620.0780.1810.0470.0910.054
COLE_CALENDARIO0.1290.4451.0000.0840.0710.1590.2420.0250.2690.0330.0160.0290.0290.0010.0520.1650.1830.3400.0410.1740.0700.0720.0520.1420.1620.2900.1070.1360.117
COLE_CARACTER0.1920.0570.0841.0000.0560.1510.3820.2020.0850.0010.0390.0090.0090.0100.0230.0850.0890.1270.0600.1260.0870.0800.0580.0650.0790.0920.0600.0670.063
COLE_GENERO0.0840.1210.0710.0561.0000.1110.1080.0350.1400.0020.1880.0100.0100.0040.0330.1300.1290.1580.0440.1360.1280.1240.0870.1180.1300.1520.1130.1170.110
COLE_JORNADA0.2400.0780.1590.1510.1111.0000.3880.0880.1590.0070.0500.0220.0220.0160.0570.1690.1570.1480.1570.2240.2170.2210.1500.1560.1770.1660.1520.1560.140
COLE_NATURALEZA0.0970.0720.2420.3820.1080.3881.0000.0990.2700.0050.0170.0260.0260.0180.0670.2780.2780.3680.1380.2450.1080.1150.0750.1920.2160.2640.1530.1700.167
COLE_SEDE_PRINCIPAL0.1530.0110.0250.2020.0350.0880.0991.0000.0480.0000.0010.0070.0070.0030.0180.0580.0630.0510.0930.0270.0610.0530.0410.0570.0670.0460.0560.0370.065
DESEMP_INGLES0.0970.1810.2690.0850.1400.1590.2700.0481.0000.0080.0800.0240.0240.0120.0510.2250.2250.2430.0780.3350.3000.3120.2130.3900.4640.8050.3610.3390.366
ESTU_ESTADOINVESTIGACION0.0160.0030.0330.0010.0020.0070.0050.0000.0081.0000.0010.0470.0470.0000.0060.0050.0050.0140.0090.0100.0040.0050.0040.0080.0070.0200.0060.0000.007
ESTU_GENERO0.0190.0130.0160.0390.1880.0500.0170.0010.0800.0011.0000.0050.0050.0170.0650.0780.0690.0650.0330.0470.0450.0490.0520.1150.1020.0810.0340.1480.080
ESTU_NACIONALIDAD0.0070.0300.0290.0090.0100.0220.0260.0070.0240.0470.0051.0001.0000.0000.0060.0140.0130.0220.0180.0220.0170.0170.0150.0150.0110.0310.0150.0280.006
ESTU_PAIS_RESIDE0.0070.0300.0290.0090.0100.0220.0260.0070.0240.0470.0051.0001.0000.0000.0060.0140.0130.0220.0180.0220.0170.0170.0150.0150.0110.0310.0150.0280.006
ESTU_PRIVADO_LIBERTAD0.0070.0000.0010.0100.0040.0160.0180.0030.0120.0000.0170.0000.0001.0000.0010.0060.0060.0050.0060.0040.0040.0050.0000.0230.0240.0110.0200.0000.016
FAMI_CUARTOSHOGAR0.0340.0280.0520.0230.0330.0570.0670.0180.0510.0060.0650.0060.0060.0011.0000.0480.0500.0910.2250.1680.2010.1930.2130.0360.0410.0430.0330.0380.033
FAMI_EDUCACIONMADRE0.1560.1130.1650.0850.1300.1690.2780.0580.2250.0050.0780.0140.0140.0060.0481.0000.3260.2310.0720.3780.3810.4070.3150.1330.1520.1600.1310.1390.124
FAMI_EDUCACIONPADRE0.1730.1270.1830.0890.1290.1570.2780.0630.2250.0050.0690.0130.0130.0060.0500.3261.0000.2390.0690.3770.3580.3780.2930.1290.1480.1560.1240.1340.121
FAMI_ESTRATOVIVIENDA0.1940.2110.3400.1270.1580.1480.3680.0510.2430.0140.0650.0220.0220.0050.0910.2310.2391.0000.0810.4730.4020.4090.3180.1470.1680.2290.1330.1610.133
FAMI_PERSONASHOGAR0.0940.0220.0410.0600.0440.1570.1380.0930.0780.0090.0330.0180.0180.0060.2250.0720.0690.0811.0000.1100.1360.2120.1580.0580.0630.0810.0570.0650.066
FAMI_TIENEAUTOMOVIL0.0210.0840.1740.1260.1360.2240.2450.0270.3350.0100.0470.0220.0220.0040.1680.3780.3770.4730.1101.0000.2840.2860.2330.2270.2530.3410.1980.2640.202
FAMI_TIENECOMPUTADOR0.1760.0290.0700.0870.1280.2170.1080.0610.3000.0040.0450.0170.0170.0040.2010.3810.3580.4020.1360.2841.0000.6600.3950.2630.2940.2840.2580.2500.255
FAMI_TIENEINTERNET0.1930.0320.0720.0800.1240.2210.1150.0530.3120.0050.0490.0170.0170.0050.1930.4070.3780.4090.2120.2860.6601.0000.4180.2350.2740.3020.2640.2640.227
FAMI_TIENELAVADORA0.1350.0230.0520.0580.0870.1500.0750.0410.2130.0040.0520.0150.0150.0000.2130.3150.2930.3180.1580.2330.3950.4181.0000.1560.1830.2080.1780.1920.145
PUNT_C_NATURALES0.0980.0620.1420.0650.1180.1560.1920.0570.3900.0080.1150.0150.0150.0230.0360.1330.1290.1470.0580.2270.2630.2350.1561.0000.9020.6650.7280.7650.776
PUNT_GLOBAL0.1250.0780.1620.0790.1300.1770.2160.0670.4640.0070.1020.0110.0110.0240.0410.1520.1480.1680.0630.2530.2940.2740.1830.9021.0000.7560.8890.8930.907
PUNT_INGLES0.0850.1810.2900.0920.1520.1660.2640.0460.8050.0200.0810.0310.0310.0110.0430.1600.1560.2290.0810.3410.2840.3020.2080.6650.7561.0000.6300.5810.645
PUNT_LECTURA_CRITICA0.1180.0470.1070.0600.1130.1520.1530.0560.3610.0060.0340.0150.0150.0200.0330.1310.1240.1330.0570.1980.2580.2640.1780.7280.8890.6301.0000.7240.775
PUNT_MATEMATICAS0.0690.0910.1360.0670.1170.1560.1700.0370.3390.0000.1480.0280.0280.0000.0380.1390.1340.1610.0650.2640.2500.2640.1920.7650.8930.5810.7241.0000.724
PUNT_SOCIALES_CIUDADANAS0.1110.0540.1170.0630.1100.1400.1670.0650.3660.0070.0800.0060.0060.0160.0330.1240.1210.1330.0660.2020.2550.2270.1450.7760.9070.6450.7750.7241.000

Missing values

2025-05-14T03:20:12.422800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-14T03:20:15.947729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-14T03:20:24.224738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ESTU_CONSECUTIVOCOLE_AREA_UBICACIONCOLE_BILINGUECOLE_CALENDARIOCOLE_CARACTERCOLE_DEPTO_UBICACIONCOLE_GENEROCOLE_JORNADACOLE_MCPIO_UBICACIONCOLE_NATURALEZACOLE_SEDE_PRINCIPALESTU_ESTADOINVESTIGACIONESTU_ESTUDIANTEESTU_GENEROESTU_MCPIO_PRESENTACIONESTU_MCPIO_RESIDEESTU_NACIONALIDADESTU_PAIS_RESIDEESTU_PRIVADO_LIBERTADFAMI_CUARTOSHOGARFAMI_EDUCACIONMADREFAMI_EDUCACIONPADREFAMI_ESTRATOVIVIENDAFAMI_PERSONASHOGARFAMI_TIENEAUTOMOVILFAMI_TIENECOMPUTADORFAMI_TIENEINTERNETFAMI_TIENELAVADORADESEMP_INGLESPUNT_INGLESPUNT_MATEMATICASPUNT_SOCIALES_CIUDADANASPUNT_C_NATURALESPUNT_LECTURA_CRITICAPUNT_GLOBAL
0SB11201720518410URBANONATÉCNICO/ACADÉMICOANTIOQUIAMIXTOSABATINAMEDELLINNO OFICIALSPUBLICARESTUDIANTEFITAGÜÍMEDELLÍNCOLOMBIACOLOMBIANCincoSecundaria (Bachillerato) incompletaPrimaria incompletaEstrato 25 a 6NoNoSiSiA-NaNNaN54.044.054.0253.0
1SB11201940215679URBANONATÉCNICO/ACADÉMICOANTIOQUIAMIXTOMAÑANAITAGÜÍOFICIALSPUBLICARESTUDIANTEMITAGÜÍITAGÜÍCOLOMBIACOLOMBIANUnoSecundaria (Bachillerato) completaPrimaria incompletaEstrato 33 a 4NoSiSiSiA1NaNNaN57.058.048.0284.0
2SB11201940215679URBANONATÉCNICO/ACADÉMICOANTIOQUIAMIXTOMAÑANAITAGÜÍOFICIALSPUBLICARESTUDIANTEMITAGÜÍITAGÜÍCOLOMBIACOLOMBIANUnoSecundaria (Bachillerato) completaPrimaria incompletaEstrato 33 a 4NoSiSiSiA1NaNNaN57.058.048.0284.0
3SB11201620355612URBANONATÉCNICOANTIOQUIAMIXTOCOMPLETAMEDELLINNO OFICIALSPUBLICARESTUDIANTEMMEDELLÍNMEDELLÍNCOLOMBIACOLOMBIANTresTécnica o tecnológica completaSecundaria (Bachillerato) completaEstrato 3CuatroNoSiSiSiB1NaNNaN73.068.068.0352.0
4SB11201220572895URBANONAACADÉMICOANTIOQUIAMIXTOMAÑANAMEDELLÍNOFICIALSPUBLICARESTUDIANTEMMEDELLÍNMEDELLÍNCOLOMBIACOLOMBIANTresPrimaria incompletaPrimaria incompletaEstrato 3CuatroNoSiNoSiA1NaNNaNNaNNaNNaNNaN
5SB11201420240659URBANONAACADÉMICOANTIOQUIAMIXTOMAÑANACOPACABANAOFICIALSPUBLICARESTUDIANTEFCOPACABANABELLOCOLOMBIACOLOMBIANTresSecundaria (Bachillerato) completaSecundaria (Bachillerato) incompletaEstrato 2CuatroNoSiSiSiA2NaNNaN53.062.069.0305.0
6SB11201220106839URBANONATÉCNICO/ACADÉMICOANTIOQUIAMIXTOTARDEMEDELLÍNOFICIALSPUBLICARESTUDIANTEFMEDELLÍNMEDELLÍNCOLOMBIACOLOMBIANTresSecundaria (Bachillerato) completaNo sabeEstrato 3CincoNoSiSiSiA-NaNNaNNaNNaNNaNNaN
7SB11201520202169URBANONOTROACADÉMICOANTIOQUIAMIXTOTARDEMEDELLINNO OFICIALSPUBLICARESTUDIANTEMMEDELLÍNMEDELLÍNCOLOMBIACOLOMBIANCincoNo sabePrimaria incompletaEstrato 2CincoNoSiSiSiA1NaNNaN50.041.057.0251.0
8SB11201620029223URBANONAACADÉMICOANTIOQUIAMIXTOSABATINAYALIOFICIALSPUBLICARESTUDIANTEFVEGACHÍYALÍCOLOMBIACOLOMBIANTresPrimaria incompletaPrimaria incompletaEstrato 1CuatroNoSiNoNoA-NaNNaN53.048.065.0264.0
9SB11201520139350URBANONAACADÉMICOANTIOQUIAMIXTOTARDEBELLOOFICIALSPUBLICARESTUDIANTEFBELLOBELLOCOLOMBIACOLOMBIANCuatroSecundaria (Bachillerato) incompletaNingunoEstrato 3CincoNoSiSiSiA-NaNNaN46.048.043.0227.0
ESTU_CONSECUTIVOCOLE_AREA_UBICACIONCOLE_BILINGUECOLE_CALENDARIOCOLE_CARACTERCOLE_DEPTO_UBICACIONCOLE_GENEROCOLE_JORNADACOLE_MCPIO_UBICACIONCOLE_NATURALEZACOLE_SEDE_PRINCIPALESTU_ESTADOINVESTIGACIONESTU_ESTUDIANTEESTU_GENEROESTU_MCPIO_PRESENTACIONESTU_MCPIO_RESIDEESTU_NACIONALIDADESTU_PAIS_RESIDEESTU_PRIVADO_LIBERTADFAMI_CUARTOSHOGARFAMI_EDUCACIONMADREFAMI_EDUCACIONPADREFAMI_ESTRATOVIVIENDAFAMI_PERSONASHOGARFAMI_TIENEAUTOMOVILFAMI_TIENECOMPUTADORFAMI_TIENEINTERNETFAMI_TIENELAVADORADESEMP_INGLESPUNT_INGLESPUNT_MATEMATICASPUNT_SOCIALES_CIUDADANASPUNT_C_NATURALESPUNT_LECTURA_CRITICAPUNT_GLOBAL
1009307AC202210005856URBANONaNBACADÉMICOANTIOQUIAMIXTOCOMPLETAENVIGADONO OFICIALSPUBLICARESTUDIANTEMMEDELLÍNMEDELLÍNCOLOMBIACOLOMBIANDosPostgradoSecundaria (Bachillerato) incompletaEstrato 51 a 2SiSiSiSiB1NaNNaN59.066.067.0329.0
1009308AC202210003792URBANONaNATÉCNICO/ACADÉMICOANTIOQUIAMIXTOSABATINAMEDELLÍNNO OFICIALSPUBLICARESTUDIANTEMMEDELLÍNMEDELLÍNCOLOMBIACOLOMBIANTresNaNNaNNaN5 a 6NoNoNaNNoA1NaNNaN50.040.053.0241.0
1009309AC202210009926RURALSBACADÉMICOANTIOQUIAMIXTOCOMPLETAENVIGADONO OFICIALSPUBLICARESTUDIANTEFMEDELLÍNENVIGADOCOLOMBIACOLOMBIANCincoPostgradoPostgradoEstrato 65 a 6SiSiSiSiB+NaNNaN60.063.074.0325.0
1009310AC202210015479URBANONAACADÉMICOANTIOQUIAMIXTOTARDEMEDELLÍNNO OFICIALSPUBLICARESTUDIANTEFMEDELLÍNMEDELLÍNCOLOMBIACOLOMBIANTresSecundaria (Bachillerato) completaSecundaria (Bachillerato) completaEstrato 13 a 4SiSiSiSiA-NaNNaN37.035.043.0194.0
1009311AC202210007081URBANONaNBACADÉMICOANTIOQUIAMIXTOCOMPLETAENVIGADONO OFICIALSPUBLICARESTUDIANTEMMEDELLÍNMEDELLÍNCOLOMBIACOLOMBIANDosPostgradoPostgradoEstrato 53 a 4SiSiSiSiB+NaNNaN77.069.066.0363.0
1009312AC202210010037RURALSBACADÉMICOANTIOQUIAMIXTOCOMPLETAENVIGADONO OFICIALSPUBLICARESTUDIANTEFMEDELLÍNENVIGADOCOLOMBIACOLOMBIANCincoPostgradoPostgradoEstrato 55 a 6SiSiSiSiB+NaNNaN66.052.063.0303.0
1009313AC202210009963RURALSBACADÉMICOANTIOQUIAMIXTOCOMPLETAENVIGADONO OFICIALSPUBLICARESTUDIANTEFMEDELLÍNENVIGADOCOLOMBIACOLOMBIANDosEducación profesional completaEducación profesional completaEstrato 61 a 2SiSiSiSiB+NaNNaN67.058.060.0321.0
1009314AC202210032143URBANONOTROACADÉMICOANTIOQUIAMIXTOSABATINAMEDELLÍNNO OFICIALSPUBLICARESTUDIANTEMMEDELLÍNSANTA FÉ DE ANTIOQUIACOLOMBIACOLOMBIANTresEducación profesional completaEducación profesional completaEstrato 33 a 4SiSiSiSiA1NaNNaN52.054.060.0269.0
1009315AC202210011473URBANONaNBACADÉMICOANTIOQUIAMIXTOCOMPLETAENVIGADONO OFICIALSPUBLICARESTUDIANTEFMEDELLÍNENVIGADOCOLOMBIACOLOMBIANNaNNaNNaNNaNNaNNaNNaNNaNNaNB1NaNNaN59.062.060.0323.0
1009316AC202210023501URBANONOTROACADÉMICOANTIOQUIAMIXTOMAÑANAMEDELLÍNNO OFICIALSPUBLICARESTUDIANTEFMEDELLÍNMEDELLÍNCOLOMBIACOLOMBIANDosPrimaria completaNo sabeEstrato 13 a 4NoSiSiSiA-NaNNaN45.040.052.0232.0

Duplicate rows

Most frequently occurring

ESTU_CONSECUTIVOCOLE_AREA_UBICACIONCOLE_BILINGUECOLE_CALENDARIOCOLE_CARACTERCOLE_DEPTO_UBICACIONCOLE_GENEROCOLE_JORNADACOLE_MCPIO_UBICACIONCOLE_NATURALEZACOLE_SEDE_PRINCIPALESTU_ESTADOINVESTIGACIONESTU_ESTUDIANTEESTU_GENEROESTU_MCPIO_PRESENTACIONESTU_MCPIO_RESIDEESTU_NACIONALIDADESTU_PAIS_RESIDEESTU_PRIVADO_LIBERTADFAMI_CUARTOSHOGARFAMI_EDUCACIONMADREFAMI_EDUCACIONPADREFAMI_ESTRATOVIVIENDAFAMI_PERSONASHOGARFAMI_TIENEAUTOMOVILFAMI_TIENECOMPUTADORFAMI_TIENEINTERNETFAMI_TIENELAVADORADESEMP_INGLESPUNT_INGLESPUNT_MATEMATICASPUNT_SOCIALES_CIUDADANASPUNT_C_NATURALESPUNT_LECTURA_CRITICAPUNT_GLOBAL# duplicates
0SB11201010024998RURALSBACADÉMICOANTIOQUIAMIXTOCOMPLETAENVIGADONO OFICIALSPUBLICARESTUDIANTEFMEDELLÍNMEDELLÍNCOLOMBIACOLOMBIANTresEducación profesional completaEducación profesional completaEstrato 6CuatroSiSiSiSiB+NaNNaNNaNNaNNaNNaN11
1SB11201010025019RURALSBACADÉMICOANTIOQUIAMIXTOCOMPLETAENVIGADONO OFICIALSPUBLICARESTUDIANTEFMEDELLÍNMEDELLÍNCOLOMBIACOLOMBIANCincoPostgradoPostgradoEstrato 6CincoSiSiSiSiB+NaNNaNNaNNaNNaNNaN11
2SB11201010025020RURALSBACADÉMICOANTIOQUIAMIXTOCOMPLETAENVIGADONO OFICIALSPUBLICARESTUDIANTEFMEDELLÍNNaNCOLOMBIACOLOMBIANNaNNaNNaNNaNNaNNaNNaNNaNNaNB+NaNNaNNaNNaNNaNNaN11
3SB11201010025022RURALSBACADÉMICOANTIOQUIAMIXTOCOMPLETAENVIGADONO OFICIALSPUBLICARESTUDIANTEFMEDELLÍNMEDELLÍNCOLOMBIACOLOMBIANTresPostgradoPostgradoEstrato 6CuatroSiSiSiSiB+NaNNaNNaNNaNNaNNaN11
4SB11201010025023RURALSBACADÉMICOANTIOQUIAMIXTOCOMPLETAENVIGADONO OFICIALSPUBLICARESTUDIANTEFMEDELLÍNMEDELLÍNCOLOMBIACOLOMBIANTresEducación profesional incompletaEducación profesional completaEstrato 6CuatroSiSiSiSiB+NaNNaNNaNNaNNaNNaN11
5SB11201010025043RURALSBACADÉMICOANTIOQUIAMIXTOCOMPLETAENVIGADONO OFICIALSPUBLICARESTUDIANTEMMEDELLÍNMEDELLÍNCOLOMBIACOLOMBIANTresEducación profesional completaEducación profesional completaEstrato 6CuatroSiSiSiSiB1NaNNaNNaNNaNNaNNaN11
6SB11201010025044RURALSBACADÉMICOANTIOQUIAMIXTOCOMPLETAENVIGADONO OFICIALSPUBLICARESTUDIANTEFMEDELLÍNMEDELLÍNCOLOMBIACOLOMBIANCincoTécnica o tecnológica completaEducación profesional incompletaEstrato 6SieteSiSiSiSiB+NaNNaNNaNNaNNaNNaN11
7SB11201010025045RURALSBACADÉMICOANTIOQUIAMIXTOCOMPLETAENVIGADONO OFICIALSPUBLICARESTUDIANTEMMEDELLÍNMEDELLÍNCOLOMBIACOLOMBIANTresEducación profesional completaEducación profesional completaEstrato 6TresSiSiSiSiB1NaNNaNNaNNaNNaNNaN11
8SB11201010025046RURALSBACADÉMICOANTIOQUIAMIXTOCOMPLETAENVIGADONO OFICIALSPUBLICARESTUDIANTEMMEDELLÍNMEDELLÍNCOLOMBIACOLOMBIANCuatroEducación profesional completaEducación profesional completaEstrato 6CincoSiSiSiSiB1NaNNaNNaNNaNNaNNaN11
9SB11201010025049RURALSBACADÉMICOANTIOQUIAMIXTOCOMPLETAENVIGADONO OFICIALSPUBLICARESTUDIANTEFMEDELLÍNMEDELLÍNCOLOMBIACOLOMBIANCuatroPostgradoPostgradoEstrato 5CincoSiSiSiSiB+NaNNaNNaNNaNNaNNaN11